There is a strong need to develop an automatic buried explosive hazards detection (EHD) system for purposes such as route clearance. In this article, we put forth a new automatic detection system, which consists of keypoint identification, feature extraction, classification and clustering. In particular, we focus on a new soft feature extraction process from forwardlooking long-wave infrared (FL-LWIR) imagery based on the use of an importance map derived from a bank of Gabor energy filters. Experiments are conducted using a variety of target types buried at varying depths at a U.S. Army test site. An uncooled LWIR camera is used and the collected data spans multiple lanes and times of day (due to diurnal temperature variation that occurs in IR). The preliminary receiver operating characteristic (ROC) curve-based performance presented herein is extremely encouraging for FL-EHD.
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Stanton R. Price ; Derek T. Anderson ; Robert H. Luke ; Kevin Stone and James M. Keller
Automatic detection system for buried explosive hazards in FL-LWIR based on soft feature extraction using a bank of Gabor energy filters
", Proc. SPIE 8709, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII, 87091B (June 7, 2013); doi:10.1117/12.2014781; http://dx.doi.org/10.1117/12.2014781